asymptotic distribution is derived and shown to be approximated by a bootstrap procedure. …

observations. Accordingly, we propose an i.i.d.-type bootstrap to determine the critical value for the test. …

We prove geometric ergodicity and absolute regularity of the nonparametric autoregressive bootstrap process. To this … sequence. This is achieved by using well-established coupling techniques. Then we apply the result to the bootstrap process and … bootstrap process has the desired properties. Moreover, by using some 'decoupling' argument, we show that the stationary density …

often not provided. If confidence intervals are given they are often based on bootstrap methods with poor theoretical …

bootstrap versions of the tests have much better properties in this respect. In other words, the bootstrap can be used to size …

fixed link functions. In this paper we consider a bootstrap specification test that detects nonparametric deviations of the … link function. The bootstrap is used with the aim to find a more accurate distribution under the null than the normal … we show that the bootstrap is able to capture the negative bias and the skewness of the test statistic. It yields better …

conditional heteroskedasticity. We show that the wild bootstrap provides convenient critical values for the considered OLS …-based statistics under both homoskedastic and conditionally heteroskedastic model errors. The wild bootstrap is easy to implement and … approximations. We prove further that the wild bootstrap retains its validity for inference within a system of pooled equations …

use quantities derived from the parameters to disentangle the relationships between the variables. Bootstrap methods are … often used for inference on the derived quantities. Alternative bootstrap methods for this purpose are discussed, some …

bootstrap for estimating the variance of the index estimator and a variant of bagging for numerically stabilizing its variance …